from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 1.863516 | 0.117614 | NaN | 0.000429 | 0.001864 | brute | -1 | 1 | 0.663 | 0.192837 | 0.009580 | 0.687 | 9.663702 | 9.675619 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 2.727397 | 0.029766 | NaN | 0.000293 | 0.002727 | brute | -1 | 5 | 0.757 | 0.187984 | 0.005655 | 0.742 | 14.508632 | 14.515196 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.036001 | 0.034826 | NaN | 0.000393 | 0.002036 | brute | 1 | 100 | 0.882 | 0.232535 | 0.003636 | 0.875 | 8.755667 | 8.756738 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.019218 | 0.000417 | NaN | 0.000042 | 0.019218 | brute | 1 | 100 | 1.000 | 0.007647 | 0.000270 | 0.000 | 2.513014 | 2.514576 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 2.735226 | 0.044376 | NaN | 0.000292 | 0.002735 | brute | -1 | 100 | 0.882 | 0.237091 | 0.006604 | 0.875 | 11.536618 | 11.541092 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.022868 | 0.002251 | NaN | 0.000035 | 0.022868 | brute | -1 | 100 | 1.000 | 0.007983 | 0.001021 | 0.000 | 2.864480 | 2.887831 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.058051 | 0.054348 | NaN | 0.000389 | 0.002058 | brute | 1 | 5 | 0.757 | 0.194857 | 0.005551 | 0.742 | 10.561844 | 10.566128 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.134952 | 0.023934 | NaN | 0.000705 | 0.001135 | brute | 1 | 1 | 0.663 | 0.193584 | 0.004843 | 0.687 | 5.862857 | 5.864691 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.678147 | 0.028749 | NaN | 0.000010 | 0.001678 | brute | -1 | 1 | 0.896 | 0.029784 | 0.000870 | 0.967 | 56.344824 | 56.368872 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.584159 | 0.029533 | NaN | 0.000006 | 0.002584 | brute | -1 | 5 | 0.922 | 0.030526 | 0.001078 | 0.974 | 84.654512 | 84.707312 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 1.978419 | 0.045057 | NaN | 0.000008 | 0.001978 | brute | 1 | 100 | 0.929 | 0.070371 | 0.003469 | 0.975 | 28.113935 | 28.148073 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.844127 | 0.092454 | NaN | 0.000006 | 0.002844 | brute | -1 | 100 | 0.929 | 0.069864 | 0.001380 | 0.975 | 40.709705 | 40.717647 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 2.103565 | 0.018946 | NaN | 0.000008 | 0.002104 | brute | 1 | 5 | 0.922 | 0.031732 | 0.001147 | 0.974 | 66.292069 | 66.335386 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.042292 | 0.014833 | NaN | 0.000015 | 0.001042 | brute | 1 | 1 | 0.896 | 0.031525 | 0.000521 | 0.967 | 33.062498 | 33.067007 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.295 | 0.0 | -1 | 1 | 0.047 | 0.004 | 0.231 | 0.232 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.101 | 0.0 | -1 | 5 | 0.047 | 0.000 | 0.240 | 0.240 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.214 | 0.0 | 1 | 100 | 0.046 | 0.001 | 0.243 | 0.243 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.269 | 0.0 | -1 | 100 | 0.046 | 0.001 | 0.237 | 0.237 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.001 | 7.325 | 0.0 | 1 | 5 | 0.045 | 0.001 | 0.241 | 0.241 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.236 | 0.0 | 1 | 1 | 0.048 | 0.000 | 0.231 | 0.231 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.346 | 0.0 | -1 | 1 | 0.009 | 0.000 | 0.500 | 0.500 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.332 | 0.0 | -1 | 5 | 0.009 | 0.000 | 0.524 | 0.525 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.360 | 0.0 | 1 | 100 | 0.009 | 0.000 | 0.477 | 0.477 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.366 | 0.0 | -1 | 100 | 0.010 | 0.000 | 0.455 | 0.455 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.343 | 0.0 | 1 | 5 | 0.009 | 0.000 | 0.507 | 0.507 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.005 | 0.000 | 0.318 | 0.0 | 1 | 1 | 0.009 | 0.000 | 0.548 | 0.548 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.864 | 0.118 | 0.000 | 0.002 | -1 | 1 | 0.193 | 0.010 | 9.664 | 9.676 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.023 | 0.001 | 0.000 | 0.023 | -1 | 1 | 0.007 | 0.000 | 3.050 | 3.051 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.727 | 0.030 | 0.000 | 0.003 | -1 | 5 | 0.188 | 0.006 | 14.509 | 14.515 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.022 | 0.002 | 0.000 | 0.022 | -1 | 5 | 0.008 | 0.000 | 2.965 | 2.965 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.036 | 0.035 | 0.000 | 0.002 | 1 | 100 | 0.233 | 0.004 | 8.756 | 8.757 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.019 | 0.000 | 0.000 | 0.019 | 1 | 100 | 0.008 | 0.000 | 2.513 | 2.515 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.735 | 0.044 | 0.000 | 0.003 | -1 | 100 | 0.237 | 0.007 | 11.537 | 11.541 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.023 | 0.002 | 0.000 | 0.023 | -1 | 100 | 0.008 | 0.001 | 2.864 | 2.888 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.058 | 0.054 | 0.000 | 0.002 | 1 | 5 | 0.195 | 0.006 | 10.562 | 10.566 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.019 | 0.001 | 0.000 | 0.019 | 1 | 5 | 0.008 | 0.000 | 2.295 | 2.295 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.135 | 0.024 | 0.001 | 0.001 | 1 | 1 | 0.194 | 0.005 | 5.863 | 5.865 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.019 | 0.001 | 0.000 | 0.019 | 1 | 1 | 0.008 | 0.001 | 2.392 | 2.401 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.678 | 0.029 | 0.000 | 0.002 | -1 | 1 | 0.030 | 0.001 | 56.345 | 56.369 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.005 | 0.002 | 0.000 | 0.005 | -1 | 1 | 0.001 | 0.000 | 6.771 | 6.786 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.584 | 0.030 | 0.000 | 0.003 | -1 | 5 | 0.031 | 0.001 | 84.655 | 84.707 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.010 | 0.005 | 0.000 | 0.010 | -1 | 5 | 0.001 | 0.000 | 14.631 | 14.699 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.978 | 0.045 | 0.000 | 0.002 | 1 | 100 | 0.070 | 0.003 | 28.114 | 28.148 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.661 | 3.685 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.844 | 0.092 | 0.000 | 0.003 | -1 | 100 | 0.070 | 0.001 | 40.710 | 40.718 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.007 | 0.002 | 0.000 | 0.007 | -1 | 100 | 0.001 | 0.000 | 8.823 | 8.849 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.104 | 0.019 | 0.000 | 0.002 | 1 | 5 | 0.032 | 0.001 | 66.292 | 66.335 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.912 | 4.934 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.042 | 0.015 | 0.000 | 0.001 | 1 | 1 | 0.032 | 0.001 | 33.062 | 33.067 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.602 | 2.616 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.826547 | 1.011824 | NaN | 0.000097 | 0.000827 | kd_tree | -1 | 1 | 0.929 | 0.102737 | 0.002954 | 0.910 | 8.045311 | 8.048637 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.012278 | 0.332108 | NaN | 0.000079 | 0.001012 | kd_tree | -1 | 5 | 0.946 | 0.179715 | 0.001791 | 0.941 | 5.632698 | 5.632978 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 5.421563 | 0.349170 | NaN | 0.000015 | 0.005422 | kd_tree | 1 | 100 | 0.951 | 0.552325 | 0.021537 | 0.940 | 9.815886 | 9.823346 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.141040 | 0.143311 | NaN | 0.000025 | 0.003141 | kd_tree | -1 | 100 | 0.951 | 0.551531 | 0.006665 | 0.940 | 5.695124 | 5.695539 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.625557 | 0.200726 | NaN | 0.000049 | 0.001626 | kd_tree | 1 | 5 | 0.946 | 0.183751 | 0.005048 | 0.941 | 8.846533 | 8.849871 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 0.859269 | 0.173876 | NaN | 0.000093 | 0.000859 | kd_tree | 1 | 1 | 0.929 | 0.098223 | 0.001575 | 0.910 | 8.748185 | 8.749310 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.027149 | 0.012476 | NaN | 0.000589 | 0.000027 | kd_tree | -1 | 1 | 0.891 | 0.000456 | 0.000050 | 0.879 | 59.584910 | 59.948561 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.024406 | 0.001728 | NaN | 0.000656 | 0.000024 | kd_tree | -1 | 5 | 0.911 | 0.000726 | 0.000034 | 0.905 | 33.609957 | 33.646873 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.038928 | 0.008036 | NaN | 0.000411 | 0.000039 | kd_tree | 1 | 100 | 0.894 | 0.005343 | 0.000023 | 0.917 | 7.285176 | 7.285242 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.037684 | 0.004508 | NaN | 0.000425 | 0.000038 | kd_tree | -1 | 100 | 0.894 | 0.006200 | 0.001436 | 0.917 | 6.078173 | 6.239117 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.022591 | 0.000297 | NaN | 0.000708 | 0.000023 | kd_tree | 1 | 5 | 0.911 | 0.000747 | 0.000024 | 0.905 | 30.248385 | 30.264077 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.020113 | 0.000474 | NaN | 0.000795 | 0.000020 | kd_tree | 1 | 1 | 0.891 | 0.000444 | 0.000025 | 0.879 | 45.251136 | 45.320874 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 2.986 | 0.098 | 0.027 | 0.0 | -1 | 1 | 0.749 | 0.037 | 3.985 | 3.990 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.624 | 0.073 | 0.022 | 0.0 | -1 | 5 | 0.737 | 0.016 | 4.915 | 4.916 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.715 | 0.080 | 0.022 | 0.0 | 1 | 100 | 0.719 | 0.008 | 5.164 | 5.165 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.796 | 0.114 | 0.021 | 0.0 | -1 | 100 | 0.738 | 0.018 | 5.143 | 5.144 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.780 | 0.060 | 0.021 | 0.0 | 1 | 5 | 0.714 | 0.014 | 5.294 | 5.295 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.727 | 0.104 | 0.021 | 0.0 | 1 | 1 | 0.727 | 0.016 | 5.124 | 5.126 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.021 | 0.0 | -1 | 1 | 0.003 | 0.001 | 0.253 | 0.280 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.027 | 0.0 | -1 | 5 | 0.001 | 0.000 | 0.501 | 0.537 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.028 | 0.0 | 1 | 100 | 0.001 | 0.001 | 0.438 | 0.528 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.028 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.594 | 0.595 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.028 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.592 | 0.593 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.028 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.604 | 0.605 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.827 | 1.012 | 0.000 | 0.001 | -1 | 1 | 0.103 | 0.003 | 8.045 | 8.049 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 10.454 | 10.887 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.012 | 0.332 | 0.000 | 0.001 | -1 | 5 | 0.180 | 0.002 | 5.633 | 5.633 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 7.697 | 7.913 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 5.422 | 0.349 | 0.000 | 0.005 | 1 | 100 | 0.552 | 0.022 | 9.816 | 9.823 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.004 | 0.001 | 0.000 | 0.004 | 1 | 100 | 0.001 | 0.000 | 5.044 | 5.203 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.141 | 0.143 | 0.000 | 0.003 | -1 | 100 | 0.552 | 0.007 | 5.695 | 5.696 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.956 | 7.125 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.626 | 0.201 | 0.000 | 0.002 | 1 | 5 | 0.184 | 0.005 | 8.847 | 8.850 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.002 | 0.001 | 0.000 | 0.002 | 1 | 5 | 0.000 | 0.000 | 4.292 | 4.431 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.859 | 0.174 | 0.000 | 0.001 | 1 | 1 | 0.098 | 0.002 | 8.748 | 8.749 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 4.019 | 4.153 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.027 | 0.012 | 0.001 | 0.000 | -1 | 1 | 0.000 | 0.000 | 59.585 | 59.949 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 25.435 | 26.520 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.024 | 0.002 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 33.610 | 33.647 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 22.209 | 22.959 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.039 | 0.008 | 0.000 | 0.000 | 1 | 100 | 0.005 | 0.000 | 7.285 | 7.285 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 5.709 | 5.948 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.038 | 0.005 | 0.000 | 0.000 | -1 | 100 | 0.006 | 0.001 | 6.078 | 6.239 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 21.252 | 22.105 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.023 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 30.248 | 30.264 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 6.669 | 6.937 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.020 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 45.251 | 45.321 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 6.275 | 6.505 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.544 | 0.075 | 30 | 0.029 | 0.0 | random | 0.434 | 0.029 | 1.252 | 1.255 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.596 | 0.022 | 30 | 0.027 | 0.0 | k-means++ | 0.461 | 0.024 | 1.293 | 1.295 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.705 | 0.230 | 30 | 0.140 | 0.0 | random | 2.630 | 0.040 | 2.170 | 2.170 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 5.881 | 0.043 | 30 | 0.136 | 0.0 | k-means++ | 2.735 | 0.021 | 2.150 | 2.150 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.000 | 30 | 0.009 | 0.000 | random | 0.0 | 0.0 | 8.155 | 13.885 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.002 | 0.000 | 30 | 0.000 | 0.002 | random | 0.0 | 0.0 | 10.176 | 14.233 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.008 | 0.000 | k-means++ | 0.0 | 0.0 | 11.271 | 12.264 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | k-means++ | 0.0 | 0.0 | 12.540 | 13.137 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.001 | 30 | 0.403 | 0.000 | random | 0.0 | 0.0 | 7.153 | 7.459 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | random | 0.0 | 0.0 | 11.868 | 12.192 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.418 | 0.000 | k-means++ | 0.0 | 0.0 | 7.080 | 7.410 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | k-means++ | 0.0 | 0.0 | 11.185 | 11.438 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.002067 | 0.000133 | 20 | 0.007741 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000472 | 0.000043 | -0.000965 | 4.375769 | 4.393852 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.001914 | 0.000158 | 20 | 0.008361 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000452 | 0.000037 | -0.000750 | 4.236402 | 4.250681 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002658 | 0.000244 | 20 | 0.300976 | 0.000003 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.001114 | 0.000073 | 0.293767 | 2.385801 | 2.390878 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002692 | 0.000136 | 20 | 0.297189 | 0.000003 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.001046 | 0.000070 | 0.256968 | 2.573105 | 2.578858 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.086 | 0.003 | 20 | 0.002 | 0.0 | random | 0.029 | 0.003 | 2.945 | 2.961 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.240 | 0.007 | 20 | 0.001 | 0.0 | k-means++ | 0.089 | 0.002 | 2.708 | 2.708 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.214 | 0.008 | 20 | 0.037 | 0.0 | random | 0.119 | 0.005 | 1.803 | 1.804 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 0.653 | 0.017 | 20 | 0.012 | 0.0 | k-means++ | 0.330 | 0.010 | 1.978 | 1.978 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.008 | 0.000 | random | 0.000 | 0.0 | 4.376 | 4.394 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | random | 0.000 | 0.0 | 12.378 | 12.861 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.008 | 0.000 | k-means++ | 0.000 | 0.0 | 4.236 | 4.251 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.002 | 0.0 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.0 | 12.899 | 13.117 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.301 | 0.000 | random | 0.001 | 0.0 | 2.386 | 2.391 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | random | 0.000 | 0.0 | 9.017 | 9.095 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.297 | 0.000 | k-means++ | 0.001 | 0.0 | 2.573 | 2.579 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.002 | 0.0 | 20 | 0.000 | 0.002 | k-means++ | 0.000 | 0.0 | 10.381 | 10.535 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000396 | 0.000384 | [20] | 2.019242 | 3.961883e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.000710 | 0.001079 | 0.55 | 0.558066 | 1.015390 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.001701 | 0.000196 | [26] | 4.702119 | 1.701361e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.005957 | 0.000676 | 0.28 | 0.285612 | 0.287444 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 11.001 | 0.341 | [20] | 0.073 | 0.000 | 1.852 | 0.034 | 5.939 | 5.940 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 0.917 | 0.587 | [26] | 0.087 | 0.001 | 0.755 | 0.039 | 1.214 | 1.216 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | [20] | 2.019 | 0.0 | 0.001 | 0.001 | 0.558 | 1.015 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | [20] | 0.013 | 0.0 | 0.000 | 0.000 | 0.389 | 0.394 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | [26] | 4.702 | 0.0 | 0.006 | 0.001 | 0.286 | 0.287 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | [26] | 1.064 | 0.0 | 0.002 | 0.000 | 0.047 | 0.048 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.009528 | 0.000265 | NaN | 8.396224 | 0.00001 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.015728 | 0.000434 | 0.122191 | 0.605813 | 0.606044 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.178 | 0.003 | 0.449 | 0.0 | 0.183 | 0.003 | 0.972 | 0.972 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.102 | 0.044 | 0.726 | 0.0 | 0.325 | 0.274 | 3.391 | 4.437 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.01 | 0.0 | 8.396 | 0.0 | 0.016 | 0.0 | 0.606 | 0.606 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.00 | 0.0 | 1.217 | 0.0 | 0.000 | 0.0 | 0.681 | 0.729 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.00 | 0.0 | 5.860 | 0.0 | 0.000 | 0.0 | 0.430 | 0.646 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.00 | 0.0 | 0.015 | 0.0 | 0.000 | 0.0 | 0.606 | 0.634 | See | See |